I am incredibly excited to be a member of the faculty at San José State University, where I am an Assistant Professor of Computational Chemistry. Previously, I worked as a postdoctoral researcher in a joint appointment with Prof. Carter T. Butts (Calit2) and Prof. Rachel W. Martin (Dept. of Chemistry) at the University of California, Irvine (UCI). I completed my Ph.D. in Chemistry at UCI, advised by Prof. Ioan Andricioaei, and my M.S., B.S., and B.A. were all completed at Villanova University. In addition to my research experience, I also have 3 years of management experience at the Merck Pharmaceutical Testing Lab, and have taught everything from physical chemistry to thermodynamics to guitar. I am also active in science education outreach for communicating the broader impacts of science to people outside of the scientific community and future scientists alike.
About the Grazioli Research Group
In a nutshell, we use computers to study chemistry and biophysics, with research interests that lie at the intersection of computational/theoretical chemistry and biophysics, physical chemistry, chemical physics, data science and structural biology. I aim to leverage my past research experience in developing enhanced sampling methodologies for molecular simulations, machine learning, and coarse-grained modeling toward building AI-driven automated discovery methods for the molecular sciences. Highly motivated students of all levels, who are interested in learning to use computers to study chemistry and biophysics, are encouraged to reach out to me via my SJSU email. No programming is experience required, as the only requirements for my group are a willingness to learn, a willingness to work with others, and tenacity!
For more details on my professional experience, please click on the link to my C.V.
Much of my work in this area has been devoted toward advancing theory and creating algorithms for calculating kinetic properties from molecular simulations. I have developed multiple enhanced sampling methodologies for molecular dynamics simulations of complex systems, such as proteins and nucleic acids.
Another main focus of my research is the development of machine learning-based methods for both setting up and interpreting the results of molecular simulations. I am also interested in developing machine learning-based methodology for interpreting experimental data.
My third research focus is the development of coarse-grained modeling techniques for molecular systems whose complexity make atomistic models intractable on the time scales of interest. I have built theoretical models for phenomena such as force-modulated catalytic activity in enzymes and protein aggregation.